Accuracy Predictor-based NAS: Several approaches perform NAS by estimating the accuracy of the network [1]}, [2]}, [3]}, [4]}.
These approaches first train a graph neural network (GNN), or a graph convolution network (GCN), to estimate the network accuracy while exploring the search space. During the searching process, the test accuracy of the sample networks is obtained from the estimator instead of doing regular training.
Although by estimating the accuracy, the NAS process is significantly accelerated, the training cost of the accuracy predictor itself remains a bottleneck.
GNN requires many training samples to achieve high accuracy, thus involving a significant overhead during training the candidate networks from the search space.
Therefore, using accuracy predictors to do NAS still suffers from excessive computation and time requirements.
